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2024/2025  KAN-CDSCO2403U  Linear Algebra and Applied Statistics

English Title
Linear Algebra and Applied Statistics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Mathematics
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 10-04-2024

Relevant links
Learning objectives
To achieve grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Demonstrate a strong understanding of mathematical concepts by solving problems involving linear algebra concepts needed for data analytics and business.
  • Exhibit deeper knowledge and understanding of the topics in vectors, matrices, and other data analytical topics
  • Solve systems of linear equations using techniques such as Gaussian elimination or echelon forms.
  • Comprehend concepts like eigenvalues and eigenvectors and their relevance to solving problems in data science
  • Conduct hypothesis testing and interpret confidence intervals to estimate parameters with a certain level of confidence.
  • Demonstrate a strong understanding of the principles of probability and probability distributions.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 2
Compulsory home assignments
Each assignment is 3-5 pages in a group of 2-4 students.
The students have to get 2 out of 3 assignments approved in order to go to the exam.

There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt to pass the activity, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one individual home assignment (max.15 pages), which will cover 2 mandatory assignments.
Examination
Linear Algebra and Applied Statistics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Autumn
Aids Closed book: no aids
However, at all written sit-in exams the student has access to the basic IT application package (Microsoft Office (minus Excel), digital pen and paper, 7-zip file manager, Adobe Acrobat, Texlive, VLC player, Windows Media Player), and the student is allowed to bring simple writing and drawing utensils (non-digital). PLEASE NOTE: Students are not allowed to communicate with others during the exam.
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Course content, structure and pedagogical approach

This course's primary focus is to provide students with the essential mathematical tools from linear algebra and applied statistics needed by the students in the Data Science programme. Linear algebra forms the foundation for many data science concepts and algorithms, while applied statistics equips the students with techniques to analyze and interpret the results effectively to draw meaningful insights from data.

 

Furthermore, this course provides knowledge about,

 

  • Discrete mathematical foundations such as set theory, functions, and relations
  • Fundamental concepts of linear algebra concepts such as vectors, vector spaces, span, basic and linear independence of vectors.
  • System of linear equations, Gaussian elimination, echelon form, and free variables
  • Matrices, dimensions, row, column, and null spaces of matrices, rank of a matrix inner Product
  • Eigenvectors and eigenvalues and their significance to diagonal matrices.
  • Elementary probability theory, standard probability distributions, conditional probability and Bayes theorem
  • Statistical concepts such as hypothesis testing, drawing conclusions from data, and statistical inference using estimation and confidence intervals.

 

The course will combine lectures, hands-on exercises, and mandatory assignments on the topics covered to solidify your understanding. The students will have ample opportunities to apply these learned concepts in real-world data science problems.

 
Description of the teaching methods
The course consists of lectures, exercises, and mandatory assignments. Both the lectures and the hands-on exercise sessions will be conducted on campus. There will be a teaching assistant/instructor providing support for the hands-on exercise sessions.

The presented theories, concepts, and methods should be applied in practice during the exercise sessions. The students will work on the mandatory assignments to consolidate their understanding of the concepts and the application of the concepts using the practical skills obtained from the hands-on exercises.
Feedback during the teaching period
In this course, feedback to the students will be provided in the following ways.

1) During the hands-on exercises following each lecture, we will review the solutions to the exercises, discuss various techniques and alternative methods for solving them, and clarify any questions from the students.

2) Students will receive Feedback on the mandatory assignments as part of their grading. Since the mandatory assignments are at the group level, the students will receive collective feedback on their group submissions.
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to lectures and exercises 48 hours
Self study and exam preparation 70 hours
Preparation for exam and exam 40 hours
Total 206 hours
Last updated on 10-04-2024